Simulation in the Supply Chain Domain : Evaluating modelling approaches

1. Simulation in the Supply Chain Domain : Evaluating modelling approaches Mr. Chris Owen Dr. Pavel Albores Dr. Doug Love Operations and Information Management Group Aston Business School Aston University, Birmingham 1

2. Background and Context • Supply chains are dynamic and complex, they involve the integration, coordination and synchronisation of activities between different business entities. • Supply chains require the transmission of both material and information between those business entities. • The characteristics of supply chains mean that simulation is a useful technique. • “their complexity obstructs analytic evaluation” (Van der Zee and Van der Vorst, 2005) • There are many simulation approaches used in the supply chain, however … • There is little advice for practitioners on when to use each method. • Much of the advice that does exist relies on custom and practice. • There is little ‘back to back’ modelling of the same problem using more than one technique. VAN DER ZEE, D. J. & VAN DER VORST, J. G. A. J. 2005. A Modeling Framework for Supply Chain Simulation: Opportunities for Improved Decision Making*. Decision Sciences, 36, 65-95.

3. Research Questions • What are the main methods of simulation used to improve supply chain performance? • What are the theoretical building blocks and assumptions that lie behind these techniques? • How does this illuminate the supply chain problem types for which certain techniques might be better suited than others? • What are the relative strengths and weaknesses of different techniques in simulating certain supply chain problem types? • What experiments can be done to test and compare alternative approaches? • How can these conclusions be used to generate recommendations for practitioners on how they should deploy these tools in achieving their supply chain objectives?

4. Research Questions • What are the main methods of simulation used to improve supply chain performance? • What are the theoretical building blocks and assumptions that lie behind these techniques? • How does this illuminate the supply chain problem types for which certain techniques might be better suited than others? • What are the relative strengths and weaknesses of different techniques in simulating certain supply chain problem types? • What experiments can be done to test and compare alternative approaches? • How can these conclusions be used to generate recommendations for practitioners on how they should deploy these tools in achieving their supply chain objectives?

5. There are three main methods of simulation in the supply chain context • Discrete Event Simulation • System Dynamics • Agent Based Modelling Classification of 100 papers from literature search on simulation and the supply chain

6. Propositions were formulated from the literature review and theoretical analysis • Proposition 1 : Discrete methods of simulation can be useful in investigating strategic problem types as well as System Dynamics; • Proposition 2 : Discrete methods of simulation can represent feedback effects in models; • Proposition 3 : System Dynamics can model supply chain problem types at the operational end of the spectrum as well as the strategic; • Proposition 4 : The nature and role of decision makers in the problem may influence the selection of simulation technique; • Proposition 5 : The purpose of the modelling (exploratory, problem solving or explanatory) may influence the selection of simulation technique.

7. Case Study Approach • The case study method was selected to test the propositions • The key areas for investigation were : • the level of the supply chain problem on a scale from strategic, tactical and operational; • the importance of feedback as a feature of the problem; • the role of decision makers in the problem and how they can be represented; • the purpose of the modelling itself, whether exploratory, descriptive or explanatory. “However, there are times when little is known about a phenomenon, current perspectives seem inadequate because they have little empirical substantiation, or they conflict with each other or common sense”. (Eisenhardt, 1989). EISENHARDT, K. M. 1989. Building Theories from Case Study Research. The Academy of Management Review, 14, 532-550.

8. A multiple case approach was used Conduct 1st case study Write individual case report Draw cross-caseconclusions Select cases Modify theory Develop theory Conduct 2nd case study Write individual case report Develop policy implications Design data collection protocol Write cross-case report Conduct remaining case studies Write individual case report YIN, R. K. 2003. Case Study Research - Design and Methods, London, Sage.

9. Case Studies

10. Case Studies Strategic Purchasing SD Model Strategic Purchasing Agent Model

11. Bullwhip SD Model Case Studies Bullwhip SD Model Bullwhip Agent Based Model

12. Case Studies Coffee Pot DES Model Coffee Pot SD Model

13. Operational DES Model Case Studies Operational SD Model

14. Findings

15. An initial guidance framework for practitioners Extending ideas from LORENZ, T. A. J., A. 2006. Towards an orientation framework in multi-paradigm modelling. 23rd International Conference of the System Dynamics Society.Nijmegen.

16. An initial guidance framework for practitioners Extending ideas from LORENZ, T. A. J., A. 2006. Towards an orientation framework in multi-paradigm modelling. 23rd International Conference of the System Dynamics Society.Nijmegen.

17. Limitations and Further Work • This research has used a limited number of cases for the analysis. One practical case has been used and three ‘typifications’ or secondary cases. More cases would clearly lead to a higher level of confidence in the generalisability of the findings. • Another potential limitation has been that the modelling has been carried out by this researcher. This means that there is some risk of bias, since this researcher may have conscious or unconscious preferences or attitudes that are leading to the focusing on certain issues and the biasing of results. • More real life cases would lead to more confidence in the findings.

18. Limitations and Further Work • This research has used a limited number of cases for the analysis. One practical case has been used and three ‘typifications’ or secondary cases. More cases would clearly lead to a higher level of confidence in the generalisability of the findings. • Another potential limitation has been that the modelling has been carried out by this researcher. This means that there is some risk of bias, since this researcher may have conscious or unconscious preferences or attitudes that are leading to the focusing on certain issues and the biasing of results. • More real life cases would lead to more confidence in the findings.

19. Summary / Conclusions • The three main methods of simulation (SD, DES and ABM) in the supply chain domain have been compared through back to back modelling of case studies • Discrete methods (DES and ABM) have been found able to model all problem types and in particular to be useful in modelling strategic problem types • System Dynamics has been found to have hard limits to its application in more operational and discrete problems • System Dynamics (as a top down approach) may be vulnerable to missing decision making at the operational level • A framework to assist practitioners in the selection of the appropriate method has been presented

20. Summary / Conclusions • The three main methods of simulation (SD, DES and ABM) in the supply chain domain have been compared through back to back modelling of case studies • Discrete methods (DES and ABM) have been found able to model all problem types and in particular to be useful in modelling strategic problem types • System Dynamics has been found to have hard limits to its application in more operational and discrete problems • System Dynamics (as a top down approach) may be vulnerable to missing decision making at the operational level • A framework to assist practitioners in the selection of the appropriate method has been presented

21. Questions ?

22. Findings